Papers

• M. Laib, R. Aggoune, R. Crespo, P. Hubsch, Steel Quality Monitoring Using Data-Driven Approaches: ArcelorMittal Case Study, 2022, Lecture Notes in Computer Science, vol 13377. Paper

• M. Kanevski, M. Laib, Unsupervised Learning of High Dimensional Environmental Data Using Local Fractality Concept. In: Del Bimbo A. et al. (eds) Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science, vol 12666. Paper

• U. Iffat, E. Roseren, M. Laib, Dealing with High Dimensional Sequence Data in Manufacturing. Procedia CIRP, 2021, 104, pp. 1298–1303. Paper

• F. Guignard, M. Laib, F. Amato, M. Kanevski, Advanced analysis of temporal data using Fisher-Shannon information: theoretical development and application in geosciences, 2020, Frontiers in Earth Science, 8:255. arXiv:1912.02452/ Paper

• F. Amato, M. Laib, F. Guignard, M. Kanevski, Analysis of air pollution time series using complexity-invariant distance and information measures, 2020, Physica A: Statistical Mechanics and its Applications, 547:124391. arXiv:1909.11484/ Paper

• M. Laib and M. Kanevski, A new algorithm for redundancy minimisation in geo-environmental data, 2019. Computers & Geosciences, 133 104328. Paper

• M. Laib, F. Guignard, M. Kanevski, L. Telesca, Community detection analysis in wind speed-monitoring systems using mutual information-based complex network, 2019/04. Chaos: An Interdisciplinary Journal of Nonlinear Science, 29 (4) p. 043107. arXiv:1809.00511/ Paper

• L. Telesca, F. Guignard, M. Laib, M. Kanevski, Analysis of temporal properties of extremes of wind measurements from 132 stations over Switzerland, 2019. arXiv:1808.08847/ Paper

• L. Telesca, M. Laib, F. Guignard, D. Mauree, M. Kanevski, Linearity versus non-linearity in high frequency multilevel wind time series measured in urban areas, Chaos, Solitons & Fractals, 120 (2019), pp. 234-244. arXiv:1808.07265 / Paper

• F. Guignard, M. Lovallo, M. Laib, J. Golay, M. Kanevski, N. Helbig, L. Telesca, Investigating the time dynamics of wind speed in complex terrains by using the Fisher–Shannon method, 2019, Physica A: Statistical Mechanics and its Applications, 523 pp. 611-621. arXiv:1807.11849 / Paper

• M. Laib, M. Kanevski, A novel filter algorithm for unsupervised feature selection based on a space filling measure. Proceedings of the 26rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), pp. 485-490, Bruges (Belgium), 2018. Paper

• M. Laib, L. Telesca, M. Kanevski, Long-range fluctuations and multifractality in connectivity density time series of a wind speed monitoring network, Chaos: An Interdisciplinary Journal of Nonlinear Science, 28 (2018) pp. 033108. arXiv:1708.04216 / Paper

• M. Laib, J. Golay, L. Telesca, M. Kanevski, Multifractal analysis of the time series of daily means of wind speed in complex regions, Chaos, Solitons & Fractals, 109 (2018) pp. 118-127, arXiv:1710.01490 / Paper

• M. Laib, L. Telesca, M. Kanevski, Periodic fluctuations in correlation-based connectivity density time series: application to wind speed-monitoring network in Switzerland, Physica A: Statistical Mechanics and its Applications, 492 (2018) pp. 1555-1569 arXiv:1708.03782 / Paper

• M. Laib, M. Kanevski, Spatial Modelling of Extreme Wind Speed Distributions in Switzerland, Energy Procedia, 97: 100-107, 2016. Paper

• I. Rezgui, Z. Gheribi-Aoulmi, M. Laib, La méthode combinatoire (s) pour la construction de quelques types de plans en blocs incomplets partiellement équilibrés et le R-package "CombinS" associé, Sciences & Technologie A– N°42, Décembre (2015), pp. 15-22. Paper

• A. Boudraa, Z. Gheribi-Aoulmi, M. Laib, Recursive Method for Construction of Resolvable Nested Designs and Uniform Designs Associated, International Journal of Research and Reviews in Applied Sciences 17 (2), 167, 2013. Paper

Conferences

• R. Aggoune, M. Laib, A Genetic Algorithm for Feature Selection Applied to Data From Multiples Sources: Application to Manufacturing Data. 23ème congrès annuel de la Société Française de Recherche Opérationnelle et d’Aide à la Décision, INSA Lyon, Feb 2022, Villeurbanne - Lyon. Abstract

• M. Laib, F. Guignard, M. Kanevski, L. Telesca, Analysis of Wind Time Series Using Network Science and Multifractal Concept, EGU General Assembly 2019, Vienna. Poster

• M. Laib, J. Golay, F. Guignard, M. Kanevski, Deep Learning for Remote Sensing Scene Classification: A Simple and High-Performance Architecture, EGU General Assembly 2018, Vienna. Poster

• M. Laib, J. Golay, L. Telesca, M. Kanevski, Spatial mapping of the multifractal parameters of wind time series in Switzerland, EGU General Assembly 2018, Vienna. Poster

• J. Golay, M. Laib, M. Kanevski, IDmining: An R Package for Mining Large Datasets with the Morisita Estimator of Intrinsic Dimension, EGU General Assembly 2018, Vienna. Poster

• M. Kanevski, M. Laib, Analysis of high dimensional environmental data using local fractality concept and machine learning, EGU General Assembly 2018, Vienna. Abstract

• M. Laib, L. Telesca, M. Kanevski, Multifractal analysis of wind speed connectivity time series, Swiss Geoscience Meeting, Davos 2017. Poster

• M. Laib, L. Telesca, M. Kanevski, Modelling environmental data using unsupervised feature selection, Spatial Statistics 2017, Lancaster. Poster

• M. Laib, M. Kanevski, Network Analysis for High Frequency Wind Speed, EGU General Assembly 2017, Vienna.

• M. Laib, M. Kanevski, Modelling Wind Data using Network and Time Series Analysis, Swiss Geoscience Meeting, Geneva 2016. Poster

• M. Laib, M. Kanevski, Analysis and Modelling of Extreme Wind Speed Distribution in Mountainous Regions, EGU General Assembly 2016, Vienna. Poster